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Data Science

Data Science

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Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

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India4 750Education2 285

πŸ“ˆ Analytical overview of Telegram channel Data Science

Channel Data Science (@sql_databases) in the English language segment is an active participant. Currently, the community unites 71 057 subscribers, ranking 2 285 in the Education category and 4 750 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 71 057 subscribers.

According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -5 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 12.26%. Within the first 24 hours after publication, content typically collects 3.01% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 8 714 views. Within the first day, a publication typically gains 2 142 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
  • Thematic interests: Content is focused on key topics such as database, learning, linkedin, udemy, 029k|.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œLearn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases”

Thanks to the high frequency of updates (latest data received on 09 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

71 057
Subscribers
-824 hours
+297 days
-530 days
Posts Archive
Data Science
Data Science
71 057
πŸ”° Explaining PostgreSQL
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πŸ”° Explaining PostgreSQL

Data Science
Data Science
71 057
πŸ”° Explaining PostgreSQL PostgreSQL is a powerful and versatile open-source relational database management system. It offers
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πŸ”° Explaining PostgreSQL PostgreSQL is a powerful and versatile open-source relational database management system. It offers advanced features, such as support for complex data types, robust concurrency control, and extensive query optimization. With its scalability, reliability, and flexibility, PostgreSQL is an excellent choice for managing and organizing your data efficiently.

Data Science
Data Science
71 057
πŸ“– Types of Databases
πŸ“– Types of Databases

Data Science
Data Science
71 057
πŸ”… PREMIUM CHANNELS -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦- πŸ”° Web Development -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦-- 221k| πŸ”° Linkedin Learning 139k| πŸ”° Udemy Premium 134k| πŸ”° Web Development -β—¦-β—¦--β—¦- 118k| πŸ”° Python 3 100k| πŸ”° JavaScript Training 089k| πŸ”° Machine Learning -β—¦-β—¦--β—¦- 068k| πŸ”° Data Analysis and Databases 068k| πŸ”° Artificial Intelligence 064k| πŸ”° React and NextJs -β—¦-β—¦--β—¦- 062k| πŸ”° Linux and DevOps 049k| πŸ”° 100 Days of Python 048k| πŸ”° OpenAI Mastery -β—¦-β—¦--β—¦- 047k| πŸ”° Business and Finance 045k| πŸ”° Best Telegram Channels 041k| πŸ”° Udemy Learning -β—¦-β—¦--β—¦- 040k| πŸ”° Zero to Mastery 040k| πŸ”° Mobile Apps 036k| πŸ”° Linkedin Learning Courses -β—¦-β—¦--β—¦- 035k| πŸ”° Codedamn Courses 034k| πŸ”° React 101 031k| πŸ”° Crypto Tutorials -β—¦-β—¦--β—¦- 030k| πŸ”° Coding Interview 025k| πŸ”° Telegram's Shorts 022k| πŸ”° Linux Training -β—¦-β—¦--β—¦- 022k| πŸ”° The Coding Space -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦-- πŸ”° Add Your Channel -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦- πŸ”° 2hrs on top & 8hrs in channel!

Data Science
Data Science
71 057
πŸ“±Data Analysis πŸ“±SQL Practice: Basic Queries

Data Science
Data Science
71 057
πŸ”… SQL Practice: Basic Queries πŸ“ Practice writing basic queries in SQL in this hands-on, interactive course with coding chal
πŸ”… SQL Practice: Basic Queries πŸ“ Practice writing basic queries in SQL in this hands-on, interactive course with coding challenges in CoderPad. 🌐 Author: David Gassner πŸ”° Level: Beginner ⏰ Duration: 17m πŸ“‹ Topics: SQL πŸ”— Join Data Analysis for more courses

Data Science
Data Science
71 057
πŸ“– Roles and Responsibilities in Big Data Technology
πŸ“– Roles and Responsibilities in Big Data Technology

Data Science
Data Science
71 057
πŸ“Š 9 Key Database Types 🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial). πŸ”— Blockchain: Secure, immut
πŸ“Š 9 Key Database Types 🌍 Spatial: Stores and queries location data (PostGIS, MongoDB Spatial). πŸ”— Blockchain: Secure, immutable ledgers (BigchainDB, IBM Blockchain). 🌐 Distributed: Scales across servers (Cassandra, Amazon DynamoDB). ⚑️ In-Memory: Lightning-fast access (Redis, Memcached, H2). πŸ—‚ NoSQL: Flexible, schema-free (MongoDB, Couchbase, HBase). πŸ“‹ Relational: Structured with tables & SQL (MySQL, PostgreSQL, Oracle). 🧩 Object-Oriented: Models complex objects (db4o, Object DB). πŸ•Έ Graph: Perfect for relationships (Neo4j, Amazon Neptune). ⏱️ Time-Series: Optimized for timestamps (InfluxDB, Prometheus). Pick the right tool for your data challenge.

Data Science
Data Science
71 057
πŸ“– 6 Steps of Data Cleaning Every Data Analyst Should Know
πŸ“– 6 Steps of Data Cleaning Every Data Analyst Should Know

Data Science
Data Science
71 057
πŸ“±Data Analysis πŸ“±NoSQL Essential Training

Data Science
Data Science
71 057
πŸ”… NoSQL Essential Training πŸ“ Get a high-level view of the basics of NoSQL, from how it differs from relational databases to
πŸ”… NoSQL Essential Training πŸ“ Get a high-level view of the basics of NoSQL, from how it differs from relational databases to its pros and cons. 🌐 Author: Melanie McGee πŸ”° Level: Beginner ⏰ Duration: 43m πŸ“‹ Topics: NoSQL πŸ”— Join Data Analysis for more courses

Data Science
Data Science
71 057
πŸ“– Data Analytic Skills that will get you hired
πŸ“– Data Analytic Skills that will get you hired

Data Science
Data Science
71 057
πŸ”… PREMIUM CHANNELS -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦- πŸ”° Web Development -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦-- 221k| πŸ”° Linkedin Learning 139k| πŸ”° Udemy Premium 134k| πŸ”° Web Development -β—¦-β—¦--β—¦- 118k| πŸ”° Python 3 100k| πŸ”° JavaScript Training 089k| πŸ”° Machine Learning -β—¦-β—¦--β—¦- 068k| πŸ”° Artificial Intelligence 068k| πŸ”° Data Analysis and Databases 064k| πŸ”° React and NextJs -β—¦-β—¦--β—¦- 061k| πŸ”° Linux and DevOps 049k| πŸ”° 100 Days of Python 048k| πŸ”° OpenAI Mastery -β—¦-β—¦--β—¦- 047k| πŸ”° Business and Finance 045k| πŸ”° Best Telegram Channels 040k| πŸ”° Udemy Learning -β—¦-β—¦--β—¦- 040k| πŸ”° Zero to Mastery 040k| πŸ”° Mobile Apps 035k| πŸ”° Linkedin Learning Courses -β—¦-β—¦--β—¦- 035k| πŸ”° Codedamn Courses 034k| πŸ”° React 101 031k| πŸ”° Crypto Tutorials -β—¦-β—¦--β—¦- 030k| πŸ”° Coding Interview 025k| πŸ”° Telegram's Shorts 022k| πŸ”° Linux Training -β—¦-β—¦--β—¦- 022k| πŸ”° The Coding Space -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦-- πŸ”° Add Your Channel -β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦--β—¦-β—¦--β—¦- πŸ”° 2hrs on top & 8hrs in channel!

Data Science
Data Science
71 057
πŸ“– Data Science
πŸ“– Data Science

Data Science
Data Science
71 057
πŸ“– Data Visualization CheatSheet
πŸ“– Data Visualization CheatSheet

Data Science
Data Science
71 057
πŸ“¦ Exercise Files

Data Science
Data Science
71 057
πŸ“±Data Analysis πŸ“±Learning Apache Airflow

Data Science
Data Science
71 057
πŸ”… Learning Apache Airflow πŸ“ Get an introduction to Apache Airflowβ€”its uses, structure, how to get it up and running, and ho
πŸ”… Learning Apache Airflow πŸ“ Get an introduction to Apache Airflowβ€”its uses, structure, how to get it up and running, and how to create and execute workflows. 🌐 Author: Janani Ravi πŸ”° Level: Advanced ⏰ Duration: 2h 10m πŸ“‹ Topics: Apache Airflow, IT Automation πŸ”— Join Data Analysis for more courses

Data Science
Data Science
71 057
Here’s a practical, code-first playbook for exploring numerical data πŸ‘‡ How we approach EDA (with Python code + outputs): - B
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Here’s a practical, code-first playbook for exploring numerical data πŸ‘‡ How we approach EDA (with Python code + outputs): - Basics: shape, dtypes, and missing values. - Descriptives: mean/median, variance, and percentiles for quick sanity checks. - Distributions: histograms, boxplots, density to spot skew and spread. - Relationships: scatter plots and a correlation heatmap to find signals. - Outliers: z-scores/IQR to flag anomalies worth investigating. - Scaling: MinMax vs Z-score depending on the model and metric. - Segments: groupby comparisons to surface patterns you miss in globals. - Decisions: tie insights back to the question and next steps. What this really means is: you get a repeatable workflow that turns raw numbers into clear hypotheses fast.

Data Science
Data Science
71 057
πŸ“ Master Excel like a pro Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost yo
πŸ“ Master Excel like a pro Here's your ultimate Excel Cheat Sheet tailored for Data Analysts. Save it, share it, and boost your productivity!